A Comprehensive Map of the Human Urinary Proteome

Arivusudar Marimuthu(Manipal Academy of Higher Education), Robert N. O’Meally(Johns Hopkins University), Raghothama Chaerkady(Johns Hopkins University), Yashwanth Subbannayya(Rajiv Gandhi University of Health Sciences), Vishalakshi Nanjappa(Institute of Bioinformatics), Praveen Kumar(Institute of Bioinformatics), Dhanashree Kelkar(Institute of Bioinformatics), Sneha M. Pinto(Institute of Bioinformatics), Rakesh Sharma(Institute of Bioinformatics), Santosh Renuse(Institute of Bioinformatics), Renu Goel(Kuvempu University), Rita Christopher(National Institute of Mental Health and Neurosciences), Bernard Delanghe(Thermo Fisher Scientific (Germany)), Robert N. Cole(Johns Hopkins University), H. C. Harsha(Institute of Bioinformatics), Akhilesh Pandey(Johns Hopkins University)
Journal of Proteome Research
April 18, 2011
Cited by 196

Abstract

The study of the human urinary proteome has the potential to offer significant insights into normal physiology as well as disease pathology. The information obtained from such studies could be applied to the diagnosis of various diseases. The high sensitivity, resolution, and mass accuracy of the latest generation of mass spectrometers provides an opportunity to accurately catalog the proteins present in human urine, including those present at low levels. To this end, we carried out a comprehensive analysis of human urinary proteome from healthy individuals using high-resolution Fourier transform mass spectrometry. Importantly, we used the Orbitrap for detecting ions in both MS (resolution 60 000) and MS/MS (resolution 15 000) modes. To increase the depth of our analysis, we characterized both unfractionated as well as lectin-enriched proteins in our experiments. In all, we identified 1,823 proteins with less than 1% false discovery rate, of which 671 proteins have not previously been reported as constituents of human urine. This data set should serve as a comprehensive reference list for future studies aimed at identification and characterization of urinary biomarkers for various diseases.


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